A self-explainable operator learning method reformulates operators as decomposable integral equations to reveal spatial input contributions to predictions in blood flow and aerodynamics problems.
Interpretability of machine-learning models in physical sciences
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Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
A self-explainable operator learning method reformulates operators as decomposable integral equations to reveal spatial input contributions to predictions in blood flow and aerodynamics problems.